Estimating Longitudinal Dispersion Coefficient of Pollutants in Open Channel Flows Using Artificial Neural Networks

Document Type : Research Paper

Authors

Abstract

The longitudinal dispersion of pollutants is one of the most effiective phases of the pollutants
dilution process, which having insight about it is of importance. The complexity of measuring
longitudinal dispersion coefficient in rivers increases the necessity of using appropriate methods of
modeling to predict it. One of the most efficient methods for modeling is the artificial neural network
which is one of the artificial intelligence techniques. In this model, without applying the complex
nonlinear equations, the dynamics of the system can be extracted and, by this way the output of the
model can be predicted. In this study, the longitudinal dispersion coefficient was predicted by
artificial neural network (ANN), using hydraulic and geometric parameters of the streams as input
parameters. Results indicated that the feed forward perceptron network had a suitable precision in
estimating the longitudinal dispersion coefficient. Sensitivity analysis indicated that in the model, for
which the ratio of velocity to the shear velocity was considered as an input variable, the determination
coefficient and error function were equal to 0.84 and 0.87%, respectively. However, in the model
with an input variable of width to flow depth ratio, the determination coefficient and error function
were obtained 0.7 and 1.01%, respectively. Therefore, the ratio of the velocity to the shear velocity
or roughness coefficient had a greater impact on longitudinal dispersion coefficient, as compared with
the last one. The proposed methodology is an efficient approach to estimate dispersion coefficient in
streams and can be implemented into mathematical models of pollutant transfer.

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References

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Water and Soil Science
Volume 26, 1-2
June 2016
Pages 225-238

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